code: def forward(self, X):
      # The forward method informs about the forward pass: how one computes outputs of the network
      # from the input and the parameters of the layers registered at init
      if not isinstance(X, torch.Tensor):
          X = torch.Tensor(X)
      batch_size = X.size(0)
      X_reshaped = torch.reshape(
          X, (batch_size, -1)
      )  # Manipulations to deal with time series format
      output = self.layers(X_reshaped)
      return torch.reshape(output, (batch_size, -1, 1))
  
file_path: tslearn/docs/examples/autodiff/plot_soft_dtw_loss_for_pytorch_nn.py
imports: import numpy
  import matplotlib.pyplot
  from tslearn.datasets import CachedDatasets
  from tslearn.metrics import SoftDTWLossPyTorch
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the MultiLayerPerceptron obj's underlying member method code with example usage: obj.forward(X) and returns: torch